scholarly journals Modelled potential forest area in the forest-steppe of central Mongolia is about three times of actual forest area

2020 ◽  
Author(s):  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Florian Schneider ◽  
Stefan Erasmi ◽  
Markus Hauck ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Arid Environment ◽  
Forest Area ◽  
Forest Steppe ◽  
Forest Edges ◽  
Climate Data ◽  
Forest Stands ◽  
Tree Biomass ◽  
Central Mongolia ◽  
The Mean

Abstract. The Mongolian forest-steppe is highly sensitive to climate change and environmental impact. The intention of this study was to identify, which geoecological parameters control forest distribution and tree growth in this semi-arid environment, and to evaluate the actual and potential tree biomass. For this purpose, we applied a combination of tree biomass and soil mapping, remote sensing and climate data analysis to a study area in the northern Khangai Mountains, central Mongolia. Forests of different landscape units and site conditions generally showed minor differences in tree biomass. We found no significant correlation between tree biomass and NDVI (normalized differentiated vegetation index). Tree biomass was reduced at forest edges, in small fragmented forest stands of the steppe-dominated area, and in large forest stands, compared to all other forest units. The tree biomass of forests on slopes ranged between 25 and 380 Mg ha−1. The mean tree biomass in forests of 10–500 ha was 199–220 Mg ha−1, whereby tree biomass at the forest edges was 50–63 Mg ha−1 less than in the interior parts of the forests. The mean tree biomass of forests > 500 ha was 182 Mg ha−1, whereas that of forests

scholarly journals Geoecological parameters indicate discrepancies between potential and actual forest area in the forest-steppe of Central Mongolia

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Florian Schneider ◽  
Stefan Erasmi ◽  
Uudus Bayarsaikhan ◽  
...  
Keyword(s):  
Forest Fires ◽  
Forest Area ◽  
Mean Annual Precipitation ◽  
Forest Steppe ◽  
Forest Stands ◽  
Tree Biomass ◽  
Living Tree ◽  
Central Mongolia ◽  
Forest Distribution ◽  
Topographic Parameters

Abstract Background Forest distribution in the forest-steppe of Mongolia depends on relief, permafrost, and climate, and is highly sensitive to climate change and anthropogenic disturbance. Forest fires and logging decreased the forest area in the forest-steppe of Mongolia. The intention of this study was to identify the geoecological parameters that control forest distribution and living-tree biomass in this semi-arid environment. Based on these parameters, we aimed to delineate the area that forest might potentially occupy and to analyse the spatial patterns of actual and potential tree biomass. Methods We used a combination of various geographic methods in conjunction with statistical analyses to identify the key parameters controlling forest distribution. In several field campaigns, we mapped tree biomass and ecological parameters in a study area within the Tarvagatai Nuruu National Park (central Mongolia). Forest areas, topographic parameters and vegetation indices were obtained from remote sensing data. Significant correlations between forest distribution and living-tree biomass on one hand, and topographic parameters, climate data, and environmental conditions on the other hand, were used to delineate the area of potential forest distribution and to estimate total living-tree biomass for this area. Results Presence of forest on slopes was controlled by the factors elevation, aspect, slope, mean annual precipitation, and mean growing-season temperature. Combining these factors allowed for estimation of potential forest area but was less suitable for tree-biomass delineation. No significant differences in mean living-tree biomass existed between sites exposed to different local conditions with respect to forest fire, exploitation, and soil properties. Tree biomass was reduced at forest edges (defined as 30 m wide belt), in small fragmented and in large forest stands. Tree biomass in the study area was 20 × 109 g (1,086 km2 forest area), whereas the potential tree biomass would reach up to 65 × 109 g (> 3168 km2). Conclusions The obtained projection suggests that the potential forest area and tree biomass under the present climatic and geoecological conditions is three times that of the present forest area and biomass. Forest fires, which mostly affected large forest stands in the upper mountains, destroyed 43% of the forest area and 45% of the living-tree biomass in the study area over the period 1986–2017.

scholarly journals Climate effects on the vitality of boreal forests at the treeline in different ecozones of Mongolia

2017 ◽  
Author(s):  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Stefan Erasmi ◽  
Dirk Nikolaus Karger ◽  
Markus Hauck
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Vegetation Index ◽  
Mean Annual Precipitation ◽  
Forest Belt ◽  
Limiting Factor ◽  
Forest Steppe ◽  
Climate Data ◽  
Southern Boundary ◽  
The Mean

Abstract. In northern Mongolia, at the southern boundary of the Siberian boreal forest belt, the distribution of steppe and forest is generally linked to climate and topography, making this region highly sensible to climate change. Detailed investigations on the limiting parameters of forest and steppe occurrence in different ecozones provide necessary information for environmental modelling and scenarios of potential landscape change. In this study, remote sensing data and gridded climate data were analyzed in order to identify distribution patterns of forest and steppe in Mongolia and to detect driving ecological factors of forest occurrence and vulnerability against environmental change. With respect to anomalies in extreme years we integrated the climate and land cover data of a 15 year period from 1999–2013. Forest distribution and vegetation vitality derived from the normalized differentiated vegetation index (NDVI) were investigated for the three ecozones with boreal forest present in Mongolia (taiga, subtaiga, and forest-steppe). In addition to the entire ecozone areas, the analysis focused on different subunits of forest and non-forested areas at the upper and lower treeline, which represent ecological borderlines of site conditions. The total cover of boreal forest in Mongolia was estimated at 73 818 km2. The upper treeline generally increases from 1800 m above sea level (a.s.l.) in the Northeast to 2700 m a.s.l. in the South. The lower treeline locally emerges at 1000 m a.s.l. in the northern taiga and is rising southward to 2500 m a.s.l. The latitudinal trend of both treelines turns into a longitudinal trend in the east of the mountains ranges due to more aridity caused by rain-shadow effects. Less vital trees were identified by NDVI at both, the upper and lower treeline in relation to the respective ecozone. The mean growing season temperature (MGST) of 7.9–8.9 °C and a minimum of 6 °C was found to be a limiting parameter at the upper treeline but negligible for the lower treeline and the total ecozones. The minimum of the mean annual precipitation (MAP) of 230–290 mm yr−1 is an important limiting factor at the lower treeline but at the upper treeline in the forest-steppe ecotone, too. In general, NDVI and MAP are lower in grassland, and MGST is higher compared to the forests in the same ecozone. An exception occurs at the upper treeline of the subtaiga and taiga, where the alpine vegetation is represented by meadow mixed with shrubs. Comparing the NDVI with climate data shows that increasing precipitation and higher temperatures generally lead to higher greenness in all ecological subunits. While the MGST is positively correlated with the MAP of the total ecozones of the forest-steppe, this correlation turns negative in the taiga ecozone. The subtaiga represents an ecological transition zone of approximately 300 mm yr−1 precipitation, which occurs independently from the MGST. Nevertheless, higher temperatures lead to higher vegetation vitality in terms of NDVI values. Climate change leads to a spatial relocation of tree communities, treelines and ecozones, thus an interpretation of future tree vitality and biomass trends directly from the recent relationships between NDVI and climate parameters is difficult. While climate plays a major role for vegetation and treeline distribution in Mongolia, the disappearing permafrost needs to be accounted for as a limiting factor for tree growth when modeling future trends of climate warming and human forest disturbance.

scholarly journals Climate effects on vegetation vitality at the treeline of boreal forests of Mongolia

2018 ◽  
Vol 15 (5) ◽  
pp. 1319-1333 ◽  
Author(s):  
Michael Klinge ◽  
Choimaa Dulamsuren ◽  
Stefan Erasmi ◽  
Dirk Nikolaus Karger ◽  
Markus Hauck
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
Human Impact ◽  
Forest Type ◽  
Forest Steppe ◽  
Paleoenvironmental Reconstruction ◽  
Climate Data ◽  
Southern Boundary ◽  
The Mean ◽  
Limiting Parameters

Abstract. In northern Mongolia, at the southern boundary of the Siberian boreal forest belt, the distribution of steppe and forest is generally linked to climate and topography, making this region highly sensitive to climate change and human impact. Detailed investigations on the limiting parameters of forest and steppe in different biomes provide necessary information for paleoenvironmental reconstruction and prognosis of potential landscape change. In this study, remote sensing data and gridded climate data were analyzed in order to identify main distribution patterns of forest and steppe in Mongolia and to detect environmental factors driving forest development. Forest distribution and vegetation vitality derived from the normalized differentiated vegetation index (NDVI) were investigated for the three types of boreal forest present in Mongolia (taiga, subtaiga and forest–steppe), which cover a total area of 73 818 km2. In addition to the forest type areas, the analysis focused on subunits of forest and nonforested areas at the upper and lower treeline, which represent ecological borders between vegetation types. Climate and NDVI data were analyzed for a reference period of 15 years from 1999 to 2013. The presented approach for treeline delineation by identifying representative sites mostly bridges local forest disturbances like fire or tree cutting. Moreover, this procedure provides a valuable tool to distinguish the potential forested area. The upper treeline generally rises from 1800 m above sea level (a.s.l.) in the northeast to 2700 m a.s.l. in the south. The lower treeline locally emerges at 1000 m a.s.l. in the northern taiga and rises southward to 2500 m a.s.l. The latitudinal gradient of both treelines turns into a longitudinal one on the eastern flank of mountain ranges due to higher aridity caused by rain-shadow effects. Less productive trees in terms of NDVI were identified at both the upper and lower treeline in relation to the respective total boreal forest type area. The mean growing season temperature (MGST) of 7.9–8.9 ∘C and a minimum MGST of 6 ∘C are limiting parameters at the upper treeline but are negligible for the lower treeline. The minimum of the mean annual precipitation (MAP) of 230–290 mm yr−1 is a limiting parameter at the lower treeline but also at the upper treeline in the forest–steppe ecotone. In general, NDVI and MAP are lower in grassland, and MGST is higher compared to the corresponding boreal forest. One exception occurs at the upper treeline of the subtaiga and taiga, where the alpine vegetation consists of mountain meadow mixed with shrubs. The relation between NDVI and climate data corroborates that more precipitation and higher temperatures generally lead to higher greenness in all ecological subunits. MGST is positively correlated with MAP of the total area of forest–steppe, but this correlation turns negative in the taiga. The limiting factor in the forest–steppe is the relative humidity and in the taiga it is the snow cover distribution. The subtaiga represents an ecological transition zone of approximately 300 mm yr−1 precipitation, which occurs independently from the MGST. Since the treelines are mainly determined by climatic parameters, the rapid climate change in inner Asia will lead to a spatial relocation of tree communities, treelines and boreal forest types. However, a direct deduction of future tree vitality, forest composition and biomass trends from the recent relationships between NDVI and climate parameters is challenging. Besides human impact, it must consider bio- and geoecological issues like, for example, tree rejuvenation, temporal lag of climate adaptation and disappearing permafrost.

scholarly journals Merging Indigenous Knowledge Systems and Station Observations to Estimate the Uncertainty of Precipitation Change in Central Mongolia

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 46 ◽  
Author(s):  
Steven Fassnacht ◽  
Arren Allegretti ◽  
Niah Venable ◽  
María Fernández-Giménez ◽  
Sukh Tumenjargal ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Rate Of Change ◽  
Forest Steppe ◽  
Rain Intensity ◽  
Robust Estimate ◽  
Central Mongolia ◽  
Climate Change Uncertainty ◽  
Consensus Score ◽  
Indigenous Knowledge Systems ◽  
The Mean

Across the globe, station-based meteorological data are analyzed to estimate the rate of change in precipitation. However, in sparsely populated regions, like Mongolia, stations are few and far between, leaving significant gaps in station-derived precipitation patterns across space and over time. We combined station data with the observations of herders, who live on the land and observe nature and its changes across the landscape. Station-based trends were computed with the Mann–Kendall significance and Theil–Sen rate of change tests. We surveyed herders about their observations of changes in rain and snowfall amounts, rain intensity, and days with snow, using a closed-ended questionnaire and also recorded their qualitative observations. Herder responses were summarized using the Potential for Conflict Index (PCI2), which computes the mean herder responses and their consensus. For one set of stations in the same forest steppe ecosystem, precipitation trends were similar and decreasing, and the herder-based PCI2 consensus score matched differences between stations. For the other station set, trends were less consistent and the PCI2 consensus did not match well, since the stations had different climates and ecologies. Herder and station-based uncertainties were more consistent for the snow variables than the rain variables. The combination of both data sources produced a robust estimate of climate change uncertainty.

scholarly journals Prediction of Maize Yield at the City Level in China Using Multi-Source Data

2021 ◽  
Vol 13 (1) ◽  
pp. 146
Author(s):  
Xinxin Chen ◽  
Lan Feng ◽  
Rui Yao ◽  
Xiaojun Wu ◽  
Jia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Maize Yield ◽  
Machine Learning Algorithms ◽  
Shortwave Radiation ◽  
Growth Stages ◽  
Climate Data ◽  
Early Peak ◽  
The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

scholarly journals Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China

2021 ◽  
Vol 10 (3) ◽  
pp. 193
Author(s):  
Zhaoqi Wang ◽  
Xiang Liu ◽  
Hao Wang ◽  
Kai Zheng ◽  
Honglin Li ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Source Region ◽  
Elevation Gradient ◽  
Climate Variation ◽  
Mean Annual Precipitation ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Vegetation Greenness ◽  
The Mean ◽  
The Impact

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns.

scholarly journals Allometric equations for estimating tree biomass in restored mixed-species Atlantic Forest stands

2014 ◽  
Vol 14 (2) ◽  
Author(s):  
Lauro Rodrigues Nogueira Júnior ◽  
Vera Lex Engel ◽  
John A. Parrotta ◽  
Antonio Carlos Galvão de Melo ◽  
Danilo Scorzoni Ré
Keyword(s):  
Atlantic Forest ◽  
Forest Restoration ◽  
Global Climate ◽  
Allometric Equations ◽  
Predictor Variables ◽  
Forest Stands ◽  
Tree Biomass ◽  
Biomass Equation ◽  
South Central ◽  
Biomass Equations

Restoration of Atlantic Forests is receiving increasing attention because of its role in both biodiversity conservation and carbon sequestration for global climate change mitigation. This study was carried out in an Atlantic Forest restoration project in the south-central region of São Paulo State - Brazil to develop allometric equations to estimate tree biomass of indigenous tree species in mixed plantations. Above and below-ground biomass (AGB and BGB, respectively), stem diameter (DBH: diameter at 1.3 m height), tree height (H: total height) and specific wood density (WD) were measured for 60 trees of 19 species. Different biomass equations (linear and nonlinear-transformed) were adjusted to estimate AGB and BGB as a function of DBH, H and WD. For estimating AGB and BGB, the linear biomass equation models were the least accurate. The transformed nonlinear biomass equation that used log DBH2, log H and log WD as predictor variables were the most accurate for AGB and the transformed nonlinear biomass equations that used log DBH2*WD as predictor variables were the most accurate for BGB. It is concluded that these adjusted equations can be used to estimate the AGB and BGB in areas of the studied project. The adjusted equations can be recommended for use elsewhere in the region for forest stands of similar age, tree size ranges, species composition and site characteristics.

scholarly journals Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1459
Author(s):  
Edouard Pignède ◽  
Philippe Roudier ◽  
Arona Diedhiou ◽  
Vami Hermann N’Guessan Bi ◽  
Arsène T. Kobea ◽  
...  
Keyword(s):  
West Africa ◽  
Ivory Coast ◽  
Vegetation Index ◽  
Cultural Practices ◽  
Machine Learning Algorithms ◽  
Climate Data ◽  
Climate Services ◽  
Climate Risks ◽  
The North ◽  
Sugarcane Yield

One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.

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